Hierarchy supervised SOM neural network applied for classification problem

Le Anh Tu, Nguyen Quang Hoan, Le Son Thai


In this paper, supervised SOM neural network was suggested, with S-SOM and S-SOM+ applied for classification problems. These networks were developed from supervised and unsupervised SOM model by Kohonen and other researchers. Hierarchy supervised SOM models were developed from the S-SOM and S-SOM+, called HS-SOM and HS-SOM+. Our improvement was inspired by the idea of finding neurons that wrongly classify samples, which created extra training branches for the representative samples of these neurons. Experiments on 11 single-label classification datasets were executed. The results showed that the suggested model classified samples with high accuracy, from 92% to 100%.


Self-organizing map, supervised learning, clustering, classification, Kohonen, neural network

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DOI: https://doi.org/10.15625/1813-9663/30/3/4080 Display counter: Abstract : 77 views. PDF : 85 views.

Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology